{"id":"W3016130027","doi":"10.1089/elj.2019.0586","title":"Behind the Screens: E-Government in American State Election Administration","year":2020,"lang":"en","type":"article","venue":"Election Law Journal Rules Politics and Policy","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Transparency (behavior); Government (linguistics); State (computer science); Context (archaeology); Administration (probate law); The Internet; Public administration; Political science; Federal election; Election law; General election; Public relations; Social media; Test (biology); Law; Computer science; Politics; Democracy; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000476484,0.0001018828,0.0001144549,0.00003909779,0.001076644,0.0003427888,0.0001321211,0.00004098615,0.00003947951],"category_scores_gemma":[0.0001312661,0.00008229844,0.00004336656,0.0002899856,0.0002668144,0.0003266922,0.00002192186,0.0002985211,0.000006962356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003362597,"about_ca_system_score_gemma":0.0002494015,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05549746,"about_ca_topic_score_gemma":0.07872666,"domain_scores_codex":[0.9984624,0.0002322653,0.0002519226,0.0001262959,0.0005585325,0.0003685657],"domain_scores_gemma":[0.9993338,0.00009457306,0.0002107106,0.00005314674,0.00005888588,0.0002488639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006151339,0.00005700449,0.007650814,0.000008777345,0.00003724157,0.00000489603,0.01326052,0.00001231064,0.0001263634,0.959375,0.0008378001,0.01856775],"study_design_scores_gemma":[0.0007272026,0.001207878,0.03550326,0.00002830396,0.00003660732,0.0000556414,0.02355759,0.0005074691,0.001092322,0.04102397,0.8959128,0.0003469699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8261017,0.0001222781,0.0001189325,0.1174536,0.0001457934,0.000162197,0.00002247254,0.00002931232,0.05584372],"genre_scores_gemma":[0.9902459,0.001087165,0.0000437607,0.006517333,0.001683735,0.00000388763,0.000002256389,0.000009142711,0.0004067561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9183511,"threshold_uncertainty_score":0.9507921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01706638695949143,"score_gpt":0.3093046715993569,"score_spread":0.2922382846398655,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}